How can LLMs learn interpretable world models for open-ended agents?
Toward Universal and Interpretable World Models for Open-ended Learning Agents
October 1, 2024
https://arxiv.org/pdf/2409.18676This paper proposes a new class of generative world models designed for open-ended learning agents. These models, structured as sparse Bayesian networks, aim to balance expressiveness with computational efficiency and interpretability.
The key points for LLM-based multi-agent systems are:
- Interpretability: The model's structure provides insights into agent decision-making, crucial for transparent AI.
- Scalability: The proposed model tackles the challenge of combinatorial explosion in complex environments, enabling agents to learn in richer settings.
- Hierarchical and Mixed Dynamics: The model supports both discrete and continuous processes, crucial for real-world applications.
- Open-Ended Learning: The model allows agents to continuously adapt and refine their understanding of the world.